The role of constraints in Hebbian learning
Neural Computation
Memory mainetenance via neuronal regulation
Neural Computation
Synaptic pruning in development: a computational account
Neural Computation
Effective Neuronal Learning with Ineffective Hebbian Learning Rules
Neural Computation
Development of Neural Network Structure with Biological Mechanisms
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
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Human and animal studies show that mammalian brains undergo massive synaptic pruning during childhood, losing about half of the synapses by puberty. We have previously shown that maintaining the network performance while synapses are deleted requires that synapses be properly modified and pruned, with the weaker synapses removed. We now show that neuronal regulation, a mechanism recently observed to maintain the average neuronal input field of a postsynaptic neuron, results in a weight-dependent synaptic modification. Under the correct range of the degradation dimension and synaptic upper bound, neuronal regulation removes the weaker synapses and judiciously modifies the remaining synapses. By deriving optimal synaptic modification functions in an excitatory-inhibitory network, we prove that neuronal regulation implements near-optimal synaptic modification and maintains the performance of a network undergoing massive synaptic pruning. These findings support the possibility that neural regulation complements the action of Hebbian synaptic changes in the self-organization of the developing brain.